Parameter recovery for a skew-normal IRT model under a Bayesian approach: hierarchical framework, prior and kernel sensitivity and sample size

Item response theory (IRT) comprises a set of statistical models which are useful in many fields, especially when there is an interest in studying latent variables (or latent traits). Usually such latent traits are assumed to be random variables and a convenient distribution is assigned to them. A very common choice for such a distribution has been the standard normal. Recently, Azevedo et al. [Bayesian inference for a skew-normal IRT model under the centred parameterization, Comput. Stat. Data Anal. 55 (2011), pp. 353-365] proposed a skew-normal distribution under the centred parameterization (SNCP) as had been studied in [R. B. Arellano-Valle and A. Azzalini, The centred parametrization for the multivariate skew-normal distribution, J. Multivariate Anal. 99(7) (2008), pp. 1362-1382], to model the latent trait distribution. This approach allows one to represent any asymmetric behaviour concerning the latent trait distribution. Also, they developed a Metropolis-Hastings within the Gibbs sampling (MHWGS) algorithm based on the density of the SNCP. They showed that the algorithm recovers all parameters properly. Their results indicated that, in the presence of asymmetry, the proposed model and the estimation algorithm perform better than the usual model and estimation methods. Our main goal in this paper is to propose another type of MHWGS algorithm based on a stochastic representation (hierarchical structure) of the SNCP studied in [N. Henze, A probabilistic representation of the skew-normal distribution, Scand. J. Statist. 13 (1986), pp. 271-275]. Our algorithm has only one Metropolis-Hastings step, in opposition to the algorithm developed by Azevedo et al., which has two such steps. This not only makes the implementation easier but also reduces the number of proposal densities to be used, which can be a problem in the implementation of MHWGS algorithms, as can be seen in [R.J. Patz and B.W. Junker, A straightforward approach to Markov Chain Monte Carlo methods for item response models, J. Educ. Behav. Stat. 24(2) (1999), pp. 146-178; R. J. Patz and B. W. Junker, The applications and extensions of MCMC in IRT: Multiple item types, missing data, and rated responses, J. Educ. Behav. Stat. 24(4) (1999), pp. 342-366; A. Gelman, G.O. Roberts, and W.R. Gilks, Efficient Metropolis jumping rules, Bayesian Stat. 5 (1996), pp. 599-607]. Moreover, we consider a modified beta prior (which generalizes the one considered in [3]) and a Jeffreys prior for the asymmetry parameter. Furthermore, we study the sensitivity of such priors as well as the use of different kernel densities for this parameter. Finally, we assess the impact of the number of examinees, number of items and the asymmetry level on the parameter recovery. Results of the simulation study indicated that our approach performed equally as well as that in [3], in terms of parameter recovery, mainly using the Jeffreys prior. Also, they indicated that the asymmetry level has the highest impact on parameter recovery, even though it is relatively small. A real data analysis is considered jointly with the development of model fitting assessment tools. The results are compared with the ones obtained by Azevedo et al. The results indicate that using the hierarchical approach allows us to implement MCMC algorithms more easily, it facilitates diagnosis of the convergence and also it can be very useful to fit more complex skew IRT models.

Response plateau models fitting via isotonic regression

Within the nutritional context, the supplementation of microminerals in bird food is often made in quantities exceeding those required in the attempt to ensure the proper performance of the animals. The experiments of type dosage x response are very common in the determination of levels of nutrients in optimal food balance and include the use of regression models to achieve this objective. Nevertheless, the regression analysis routine, generally, uses a priori information about a possible relationship between the response variable. The isotonic regression is a method of estimation by least squares that generates estimates which preserves data ordering. In the theory of isotonic regression this information is essential and it is expected to increase fitting efficiency. The objective of this work was to use an isotonic regression methodology, as an alternative way of analyzing data of Zn deposition in tibia of male birds of Hubbard lineage. We considered the models of plateau response of polynomial quadratic and linear exponential forms. In addition to these models, we also proposed the fitting of a logarithmic model to the data and the efficiency of the methodology was evaluated by Monte Carlo simulations, considering different scenarios for the parametric values. The isotonization of the data yielded an improvement in all the fitting quality parameters evaluated. Among the models used, the logarithmic presented estimates of the parameters more consistent with the values reported in literature.

Predicting the behaviour of structures under impact loads using geometrically distorted scaled models

When a scaled structure (model or replica) is used to predict the response of a full-size compound (prototype), the model geometric dimensions should relate to the corresponding prototype dimensions by a single scaling factor. However, owing to manufacturing technical restrictions, this condition cannot be accomplished for some of the dimensions in real structures. Accordingly, the distorted geometry will not comply with the overall geometric scaling factor, infringing the Pi theorem requirements for complete dynamic similarity. In the present study, a method which takes geometrical distortions into account is introduced, leading to a model similar to the prototype. As a means to infer the performance of this method, three analytical problems of structures subjected to dynamic loads are analysed. It is shown that the replica developed applying this technique is able to accurately predict the full-size structure behaviour even when the studied models have some of their dimensions severely distorted. (C) 2012 Elsevier Ltd. All rights reserved.

MHD numerical simulations of colliding winds in massive binary systems - I. Thermal versus non-thermal radio emission

In the past few decades detailed observations of radio and X-ray emission from massive binary systems revealed a whole new physics present in such systems. Both thermal and non-thermal components of this emission indicate that most of the radiation at these bands originates in shocks. O and B-type stars and WolfRayet (WR) stars present supersonic and massive winds that, when colliding, emit largely due to the freefree radiation. The non-thermal radio and X-ray emissions are due to synchrotron and inverse Compton processes, respectively. In this case, magnetic fields are expected to play an important role in the emission distribution. In the past few years the modelling of the freefree and synchrotron emissions from massive binary systems have been based on purely hydrodynamical simulations, and ad hoc assumptions regarding the distribution of magnetic energy and the field geometry. In this work we provide the first full magnetohydrodynamic numerical simulations of windwind collision in massive binary systems. We study the freefree emission characterizing its dependence on the stellar and orbital parameters. We also study self-consistently the evolution of the magnetic field at the shock region, obtaining also the synchrotron energy distribution integrated along different lines of sight. We show that the magnetic field in the shocks is larger than that obtained when the proportionality between B and the plasma density is assumed. Also, we show that the role of the synchrotron emission relative to the total radio emission has been underestimated.

PD controller synthesis from open-loop response measurements of rotating system

In this work, a method of computing PD stabilising gains for rotating systems is presented based on the D-decomposition technique, which requires the sole knowledge of frequency response functions. By applying this method to a rotating system with electromagnetic actuators, it is demonstrated that the stability boundary locus in the plane of feedback gains can be easily plotted, and the most suitable gains can be found to minimise the resonant peak of the system. Experimental results for a Laval rotor show the feasibility of not only controlling lateral shaft vibration and assuring stability, but also helps in predicting the final vibration level achieved by the closed-loop system. These results are obtained based solely on the input-output response information of the system as a whole.

Design-based random permutation models with auxiliary information

We extend the random permutation model to obtain the best linear unbiased estimator of a finite population mean accounting for auxiliary variables under simple random sampling without replacement (SRS) or stratified SRS. The proposed method provides a systematic design-based justification for well-known results involving common estimators derived under minimal assumptions that do not require specification of a functional relationship between the response and the auxiliary variables.

Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.

Plasma corticosterone levels in mouse models of pain

Background: Pain markedly activates the hypothalamic-pituitary-adrenal (HPA) axis and increases plasma corticosterone release interfering significantly with nociceptive behaviour as well as the mechanism of action of analgesic drugs. Aims/Methods: In the present study, we monitored the time course of circulating corticosterone in two mouse strains (C57Bl/6 and Balb/C) under different pain models. In addition, the stress response was investigated following animal handling, intrathecal (i.t.) manipulation and habituation to environmental conditions commonly used in nociceptive experimental assays. We also examined the influence of within-cage order of testing on plasma corticosterone. Results: Subcutaneous injection of capsaicin precipitated a prompt stress response whereas carrageenan and complete Freund's adjuvant induced an increased corticosterone release around the third hour post-injection. However, carrageenan induced a longer increased corticosterone in C57Bl/6 mice. In partial sciatic nerve ligation, neuropathic pain model corticosterone increased only in the first days whereas mechanical hypersensitivity remained much longer. Animal handling also represents an important stressor whereas the i.t. injection per se does not exacerbate the handling-induced stress response. Moreover, the order of testing animals from the same cage does not interfere with plasma corticosterone levels in the intrathecal procedure. Animal habituation to the testing apparatus also does not reduce the immediate corticosterone increase as compared with non-habituated mice. Conclusion: Our data indicate that HPA axis activation in acute and chronic pain models is time dependent and may be dissociated from evoked hyperalgesia. Therefore, HPA-axis activation represents an important variable to be considered when designing experimental assays of persistent pain as well as for interpretation of data.

Assessment of variance components in nonlinear mixed-effects elliptical models

The issue of assessing variance components is essential in deciding on the inclusion of random effects in the context of mixed models. In this work we discuss this problem by supposing nonlinear elliptical models for correlated data by using the score-type test proposed in Silvapulle and Silvapulle (1995). Being asymptotically equivalent to the likelihood ratio test and only requiring the estimation under the null hypothesis, this test provides a fairly easy computable alternative for assessing one-sided hypotheses in the context of the marginal model. Taking into account the possible non-normal distribution, we assume that the joint distribution of the response variable and the random effects lies in the elliptical class, which includes light-tailed and heavy-tailed distributions such as Student-t, power exponential, logistic, generalized Student-t, generalized logistic, contaminated normal, and the normal itself, among others. We compare the sensitivity of the score-type test under normal, Student-t and power exponential models for the kinetics data set discussed in Vonesh and Carter (1992) and fitted using the model presented in Russo et al. (2009). Also, a simulation study is performed to analyze the consequences of the kurtosis misspecification.

Hydrothermal pretreatment of sugarcane bagasse using response surface methodology improves digestibility and ethanol production by SSF

Sugarcane bagasse was characterized as a feedstock for the production of ethanol using hydrothermal pretreatment. Reaction temperature and time were varied between 160 and 200A degrees C and 5-20 min, respectively, using a response surface experimental design. The liquid fraction was analyzed for soluble carbohydrates and furan aldehydes. The solid fraction was analyzed for structural carbohydrates and Klason lignin. Pretreatment conditions were evaluated based on enzymatic extraction of glucose and xylose and conversion to ethanol using a simultaneous saccharification and fermentation scheme. SSF experiments were conducted with the washed pretreated biomass. The severity of the pretreatment should be sufficient to drive enzymatic digestion and ethanol yields, however, sugars losses and especially sugar conversion into furans needs to be minimized. As expected, furfural production increased with pretreatment severity and specifically xylose release. However, provided that the severity was kept below a general severity factor of 4.0, production of furfural was below an inhibitory concentration and carbohydrate contents were preserved in the pretreated whole hydrolysate. There were significant interactions between time and temperature for all the responses except cellulose digestion. The models were highly predictive for cellulose digestibility (R (2) = 0.8861) and for ethanol production (R (2) = 0.9581), but less so for xylose extraction. Both cellulose digestion and ethanol production increased with severity, however, high levels of furfural generated under more severe pretreatment conditions favor lower severity pretreatments. The optimal pretreatment condition that gave the highest conversion yield of ethanol, while minimizing furfural production, was judged to be 190A degrees C and 17.2 min. The whole hydrolysate was also converted to ethanol using SSF. To reduce the concentration of inhibitors, the liquid fraction was conditioned prior to fermentation by removing inhibitory chemicals using the fungus Coniochaeta ligniaria.

Polarimetry of the binary PDS 144

Aims. Our goal is to study the circumstellar environment associated with each component of the wide intermediate-mass pre-main sequence binary system PDS 144 using broadband polarimetry. Methods. We present near-infrared (NIR) linear polarimetric observations of PDS 144 gathered with the IAGPOL imaging polarimeter along with the CamIV infrared camera at the Observatorio do Pico dos Dias (OPD). In addition, we re-analyzed OPD archive optical polarization to separate the binary and estimate the interstellar polarization using foreground stars. Results. After discounting the interstellar component, we found that both stars of the binary system are intrinsically polarized. The polarization vectors at optical and NIR bands of both components are aligned with the local magnetic field and the jet axis. These findings indicate an interplay between the interstellar magnetic field and the formation of the binary system. We also found that the PDS 144N is less polarized than its southern companion in the optical. However, in the NIR PDS 144N is more polarized. Our polarization data can only be explained by high inclinations (i greater than or similar to 80 degrees) for the disks of both members. In particular, comparisons of our NIR data with young stellar objects disk models suggest predominantly small grains in the circumstellar environment of PDS 144N. In spite of the different grain types in each component, the infrared spectral indexes indicate a coeval system. We also found evidence of coplanarity between the disks.

Changes in intrapopulation resource use patterns of an endangered raptor in response to a disease-mediated crash in prey abundance

1. A long-standing question in ecology is how natural populations respond to a changing environment. Emergent optimal foraging theory-based models for individual variation go beyond the population level and predict how its individuals would respond to disturbances that produce changes in resource availability. 2. Evaluating variations in resource use patterns at the intrapopulation level in wild populations under changing environmental conditions would allow to further advance in the research on foraging ecology and evolution by gaining a better idea of the underlying mechanisms explaining trophic diversity. 3. In this study, we use a large spatio-temporal scale data set (western continental Europe, 19682006) on the diet of Bonellis Eagle Aquila fasciata breeding pairs to analyse the predator trophic responses at the intrapopulation level to a prey population crash. In particular, we borrow metrics from studies on network structure and intrapopulation variation to understand how an emerging infectious disease [the rabbit haemorrhagic disease (RHD)] that caused the density of the eagles primary prey (rabbit Oryctolagus cuniculus) to dramatically drop across Europe impacted on resource use patterns of this endangered raptor. 4. Following the major RHD outbreak, substantial changes in Bonellis Eagles diet diversity and organisation patterns at the intrapopulation level took place. Dietary variation among breeding pairs was larger after than before the outbreak. Before RHD, there were no clusters of pairs with similar diets, but significant clustering emerged after RHD. Moreover, diets at the pair level presented a nested pattern before RHD, but not after. 5. Here, we reveal how intrapopulation patterns of resource use can quantitatively and qualitatively vary, given drastic changes in resource availability. 6. For the first time, we show that a pathogen of a prey species can indirectly impact the intrapopulation patterns of resource use of an endangered predator.

Latent residual analysis in binary regression with skewed link

Model diagnostics is an integral part of model determination and an important part of the model diagnostics is residual analysis. We adapt and implement residuals considered in the literature for the probit, logistic and skew-probit links under binary regression. New latent residuals for the skew-probit link are proposed here. We have detected the presence of outliers using the residuals proposed here for different models in a simulated dataset and a real medical dataset.

Comparative analysis of the pathological events involved in immune and non-immune TRALI models

Background and Objectives Transfusion-related acute lung injury (TRALI) is characterized by leukocyte transmigration and alveolar capillary leakage shortly after transfusion. TRALI pathogenesis has not been fully elucidated. In some cases, the infusion of alloantibodies (immune model), whereas in others the combination of neutrophil priming by proinflammatory molecules with the subsequent infusion of biological response modifiers (BRMs) in the hemocomponent (non-immune model) have been implicated. Our aim was to compare the pathological events involved in TRALI induced by antibodies or BRMs using murine models. Materials and Methods In the immune model, human HNA-2+ neutrophils were incubated in vitro with a monoclonal antibody (anti-CD177, clone 7D8) directed against the HNA-2 antigen and injected i.v. in NOD/SCID mice. In the non-immune model, BALB/c mice were treated with low doses of lipopolysaccharide (LPS) followed by platelet-activating factor (PAF) infusion 2 h later. Forty minutes after PAF administration, or 6 h after neutrophil injection, lungs were isolated and histological analysis, determination of a variety of cytokines and chemokines including keratinocyte-derived chemokine (KC), MIP-2, the interleukins IL-1 beta, IL-6, IL-8 as well as TNFa, cell influx and alveolar capillary leakage were performed. Results In both models, characteristic histological findings of TRALI and an increase in KC and MIP-2 levels were detected. In contrast to the immune model, in the non-immune model, there was a dramatic increase in IL-1 beta and TNFa. However, capillary leakage was only detected if PAF was administrated. Conclusions Regardless of the triggering event(s), KC, MIP-2 and integrins participate in TRALI pathogenesis, whereas PAF is essential for capillary leakage when two events are involved.

Heterogeneous time-dependent response of adipose tissue during the development of cancer cachexia

Cancer cachexia induces loss of fat mass that accounts for a large part of the dramatic weight loss observed both in humans and in animal models; however, the literature does not provide consistent information regarding the set point of weight loss and how the different visceral adipose tissue depots contribute to this symptom. To evaluate that, 8-week-old male Wistar rats were subcutaneously inoculated with 1 ml (2 x 10(7)) of tumour cells (Walker 256). Samples of different visceral white adipose tissue (WAT) depots were collected at days 0, 4, 7 and 14 and stored at -80 degrees C (seven to ten animals/each day per group). Mesenteric and retroperitoneal depot mass was decreased to the greatest extent on day 14 compared with day 0. Gene and protein expression of PPAR gamma(2) (PPARG) fell significantly following tumour implantation in all three adipose tissue depots while C/EBP alpha (CEBPA) and SREBP-1c (SREBF1) expression decreased over time only in epididymal and retroperitoneal depots. Decreased adipogenic gene expression and morphological disruption of visceral WAT are further supported by the dramatic reduction in mRNA and protein levels of perilipin. Classical markers of inflammation and macrophage infiltration (f4/80, CD68 and MIF-1 alpha) in WAT were significantly increased in the later stage of cachexia (although showing a incremental pattern along the course of cachexia) and presented a depot-specific regulation. These results indicate that impairment in the lipid-storing function of adipose tissue occurs at different times and that the mesenteric adipose tissue is more resistant to the 'fat-reducing effect' than the other visceral depots during cancer cachexia progression. Journal of Endocrinology (2012) 215, 363-373